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New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets

Author

Listed:
  • Roberto Patuelli

    (Department of Spatial Economics, Vrije Universiteit Amsterdam)

  • Aura Reggiani

    (Department of Economics, University of Bologna, Italy)

  • Peter Nijkamp

    (Department of Spatial Economics, Vrije Universiteit Amsterdam)

  • Uwe Blien

    (Institut für Arbeitsmarkt und Berufsforschung (IAB), Nuremberg)

Abstract

In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. NNs are modern statistical tools based on learning algorithms that are able to process large amounts of data. NNs are enjoying increasing interest in several fields, because of their effectiveness in handling complex data sets when the functional relationship between dependent and independent variables is not explicitly specified. The present paper compares two NN methodologies. First, it uses NNs to forecast regional employment in both the former West and East Germany. Each model implemented computes single estimates of employment growth rates for each German district, with a 2-year forecasting range. Next, additional forecasts are computed, by combining the NN methodology with Shift-Share Analysis (SSA). Since SSA aims to identify variations observed among the labour districts, its results are used as further explanatory variables in the NN models. The data set used in our experiments consists of a panel of 439 German districts. Because of differences in the size and time horizons of the data, the forecasts for West and East Germany are computed separately. The out-of-sample forecasting ability of the models is evaluated by means of several appropriate statistical indicators.

Suggested Citation

  • Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets," Tinbergen Institute Discussion Papers 06-020/3, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20060020
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    Cited by:

    1. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    2. Gian Zaccomer & Pamela Mason, 2011. "A new spatial shift-share decomposition for the regional growth analysis: a local study of the employment based on Italian Business Statistical Register," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 329-356, August.
    3. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    4. Jean‐François Ruault & Yves Schaeffer, 2020. "Scalable shift‐share analysis: Novel framework and application to France," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1667-1690, December.
    5. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
    6. Buda, Rodolphe, 2008. "Estimation de l'emploi régional et sectoriel salarié français : application à l'année 2006 [Estimation of the french salaried regional and sectoral employment: application to the year 2006]," MPRA Paper 34881, University Library of Munich, Germany.
    7. Constantin Ilie & Margareta Ilie, 2022. "Brief Analysis of the Evolution of Female Employees in Recent Years. Research Using Mathematical Modelling," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 591-597, September.
    8. Katharina Hampel & Marcus Kunz & Norbert Schanne & Ruediger Wapler & Antje Weyh, 2006. "Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation," ERSA conference papers ersa06p196, European Regional Science Association.
    9. Matthias Firgo & Oliver Fritz, 2017. "Does having the right visitor mix do the job? Applying an econometric shift-share model to regional tourism developments," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(3), pages 469-490, May.
    10. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.

    More about this item

    Keywords

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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